Exploration of COVID-19 tracking data from multiple resources
Wei Sun
2020-06-15
1 Introduction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
2 JHU
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
2.1 time series data
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
2.2 daily reports data
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
3 NY Times
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-06-13"3.1 state level data
First check the 30 states with the largest number of deaths.
## date state fips cases deaths
## 5658 2020-06-13 New York 36 387402 30565
## 5656 2020-06-13 New Jersey 34 166605 12589
## 5647 2020-06-13 Massachusetts 25 105395 7576
## 5639 2020-06-13 Illinois 17 133117 6491
## 5665 2020-06-13 Pennsylvania 42 82988 6264
## 5648 2020-06-13 Michigan 26 66024 6017
## 5629 2020-06-13 California 6 150418 5059
## 5631 2020-06-13 Connecticut 9 44994 4186
## 5644 2020-06-13 Louisiana 22 46396 3004
## 5646 2020-06-13 Maryland 24 61935 2926
## 5634 2020-06-13 Florida 12 73544 2924
## 5662 2020-06-13 Ohio 39 40848 2554
## 5640 2020-06-13 Indiana 18 40535 2413
## 5635 2020-06-13 Georgia 13 54178 2411
## 5671 2020-06-13 Texas 48 88120 1989
## 5630 2020-06-13 Colorado 8 29002 1598
## 5675 2020-06-13 Virginia 51 53869 1541
## 5649 2020-06-13 Minnesota 27 30203 1314
## 5676 2020-06-13 Washington 53 26920 1216
## 5627 2020-06-13 Arizona 4 34773 1190
## 5659 2020-06-13 North Carolina 37 42911 1135
## 5651 2020-06-13 Missouri 29 16327 890
## 5650 2020-06-13 Mississippi 28 19348 889
## 5667 2020-06-13 Rhode Island 44 15947 833
## 5625 2020-06-13 Alabama 1 24601 773
## 5678 2020-06-13 Wisconsin 55 22638 694
## 5641 2020-06-13 Iowa 19 23792 651
## 5668 2020-06-13 South Carolina 45 17955 599
## 5643 2020-06-13 Kentucky 21 12605 527
## 5633 2020-06-13 District of Columbia 11 9709 511For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
3.2 county level data
First check the 50 counties with the largest number of deaths.
## date county state fips cases deaths
## 232498 2020-06-13 New York City New York NA 214242 21551
## 231311 2020-06-13 Cook Illinois 17031 84581 4173
## 230915 2020-06-13 Los Angeles California 6037 72023 2890
## 232002 2020-06-13 Wayne Michigan 26163 21711 2669
## 232497 2020-06-13 Nassau New York 36059 41172 2668
## 232517 2020-06-13 Suffolk New York 36103 40615 1996
## 231914 2020-06-13 Middlesex Massachusetts 25017 23156 1748
## 232423 2020-06-13 Essex New Jersey 34013 18336 1741
## 232418 2020-06-13 Bergen New Jersey 34003 18805 1664
## 232525 2020-06-13 Westchester New York 36119 34252 1535
## 232922 2020-06-13 Philadelphia Pennsylvania 42101 24338 1502
## 231014 2020-06-13 Fairfield Connecticut 9001 16277 1345
## 231015 2020-06-13 Hartford Connecticut 9003 11189 1321
## 232425 2020-06-13 Hudson New Jersey 34017 18717 1253
## 232436 2020-06-13 Union New Jersey 34039 16337 1121
## 232428 2020-06-13 Middlesex New Jersey 34023 16385 1074
## 231983 2020-06-13 Oakland Michigan 26125 11298 1067
## 231018 2020-06-13 New Haven Connecticut 9009 12021 1041
## 231910 2020-06-13 Essex Massachusetts 25009 15573 1041
## 232432 2020-06-13 Passaic New Jersey 34031 16612 997
## 231918 2020-06-13 Suffolk Massachusetts 25025 19299 943
## 231970 2020-06-13 Macomb Michigan 26099 7035 889
## 231916 2020-06-13 Norfolk Massachusetts 25021 8860 882
## 231920 2020-06-13 Worcester Massachusetts 25027 11961 863
## 231070 2020-06-13 Miami-Dade Florida 12086 21632 822
## 232431 2020-06-13 Ocean New Jersey 34029 9222 813
## 232917 2020-06-13 Montgomery Pennsylvania 42091 7865 768
## 232030 2020-06-13 Hennepin Minnesota 27053 10069 712
## 231446 2020-06-13 Marion Indiana 18097 10736 693
## 231896 2020-06-13 Montgomery Maryland 24031 13573 686
## 232429 2020-06-13 Monmouth New Jersey 34025 8720 667
## 232894 2020-06-13 Delaware Pennsylvania 42045 6894 664
## 231912 2020-06-13 Hampden Massachusetts 25013 6460 637
## 232943 2020-06-13 Providence Rhode Island 44007 11959 637
## 232430 2020-06-13 Morris New Jersey 34027 6568 635
## 231897 2020-06-13 Prince George's Maryland 24033 17745 634
## 231917 2020-06-13 Plymouth Massachusetts 25023 8478 621
## 233570 2020-06-13 King Washington 53033 8702 593
## 232483 2020-06-13 Erie New York 36029 6753 573
## 230814 2020-06-13 Maricopa Arizona 4013 17791 549
## 232880 2020-06-13 Bucks Pennsylvania 42017 5419 542
## 232427 2020-06-13 Mercer New Jersey 34021 7323 517
## 231834 2020-06-13 Orleans Louisiana 22071 7374 516
## 231027 2020-06-13 District of Columbia District of Columbia 11001 9709 511
## 231908 2020-06-13 Bristol Massachusetts 25005 7906 507
## 232270 2020-06-13 St. Louis Missouri 29189 5506 500
## 232509 2020-06-13 Rockland New York 36087 13411 466
## 231824 2020-06-13 Jefferson Louisiana 22051 8339 465
## 232434 2020-06-13 Somerset New Jersey 34035 4756 436
## 231317 2020-06-13 DuPage Illinois 17043 8402 430For these 50 counties, I check the number of new cases and the number of new deaths.
4 COVID Trackng
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they can be much more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
The grades of the states may change over timea and I strongly recommend checking their webiste before puting serious interpretation on the following plot.
5 Session information
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.3.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 lattice_0.20-38 nlme_3.1-144 evaluate_0.14
## [9] lifecycle_0.2.0 tibble_3.0.1 gtable_0.3.0 mgcv_1.8-31
## [13] pkgconfig_2.0.3 rlang_0.4.6 Matrix_1.2-18 yaml_2.2.1
## [17] xfun_0.12 gridExtra_2.3 withr_2.1.2 stringr_1.4.0
## [21] dplyr_0.8.4 knitr_1.28 vctrs_0.3.0 cowplot_1.0.0
## [25] grid_3.6.2 tidyselect_1.0.0 glue_1.3.1 R6_2.4.1
## [29] rmarkdown_2.1 purrr_0.3.3 farver_2.0.3 splines_3.6.2
## [33] scales_1.1.0 ellipsis_0.3.0 htmltools_0.4.0 assertthat_0.2.1
## [37] colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3 stringi_1.4.5
## [41] munsell_0.5.0 crayon_1.3.4